Application of the HEC-HMS hydrological model in the Beht ...

Application of the HEC-HMS hydrological model in the Beht watershed (Morocco)

Fatima Daide1*, Rachida Afgane1, Abderrahim Lahrach1, Abdel-Ali Chaouni2, Mohamed Msaddek1, Ismail Elhasnaoui3

1Functional Ecology & Environmental Engineering laboratory, Sidi Mohamed Ben Abdellah University, Morocco 2 Intelligent Systems, Georesources & Renewable Energies laboratory Sidi Mohamed Ben Abdellah University, Morocco 3 Mohammed V University, Mohammadia School of Engineers, Rabat, Morocco

Abstract. This work focused on the collection and preparation of the data required for the hydrological modelling of the Beht catchment area, which covers an area of 4560 km2 with a perimeter of 414 km, by

combining the various spatial technologies, in particular geographical information systems (GIS), remote sensing, and digital terrain models (DTM), with hydrological models in order to prepare for spatial hydrological modelling used for flood forecasting. The methodology consists, at first, in the automatic extraction of the sub-basins and the drainage network. Then, edit these data using the HEC-GEO-HMS extension, and the preparation of the land use and land cover data for the elaboration of a Curve Number (CN) map of Beht watershed, then the import of the basin model into the Hydrologic Modeling System (HEC-HMS) to simulate the surface runoff using six extreme daily time series events.

Keywords: Spatial hydrological modelling; Remote sensing; Geographic Information Systems (GIS); Digital Terrain Models (DTM); Curve Number (CN); HEC-GEO HMS; HEC-HMS.

*Corresponding author:[email protected]

© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0

(http://creativecommons.org/licenses/by/4.0/).

E3S Web of Conferences 314, 05003 (2021) https://doi.org/10.1051/e3sconf/202131405003 WMAD21

1 Introduction

Over the last years, Morocco has experienced a number

of tragic flood events that have generated flooding in

several regions of the country due, on the one hand, to

population growth and urban, agricultural, industrial and tourism development which lead to an increasing

occupation of vulnerable areas and, on the other hand,

to the aggravation of extreme conditions (drought and

floods) as a result of climate change [1]. To deal with

this flood risk, a set of tools has been developed to

understand the hydrological functioning of basins. In

this context, hydrological modeling is the most

adequate tool to understand the water cycle on small and large scales.

The Soil Conservation Service (SCS) curve number

(CN) method is one of the most popular methods for

computing the runoff volume from a rainstorm [2]. It is popular because it is simple, easy to understand and

apply, and stable, and accounts for most of the runoff

producing watershed characteristics, such as soil type,

land use, hydrologic condition, and antecedent

moisture condition [3, 4]. The SCS-CN method was

originally developed for its use on small agricultural

watersheds and has since been extended and applied to

rural, forest and urban watersheds [5]. Due to its low

input data requirements and its implementation within

GIS, it has been incorporated in many widely used

hydrological models. In recent years, the method has received much attention in the hydrologic literature.

The SCS-CN method was first published in 1956 in

Section-4 of the National Engineering Handbook of

Soil Conservation Service (now called the Natural

Resources Conservation Service), U. S. Department of

Agriculture. The publication has since been revised

several times. Despite several limitations of the method

and even questionable credibility at times, it has been

in continuous use for the simple reason that it works fairly well at the field level. [6, 7, 8, 9, 10, 11]

The Hydrologic Modeling System HEC-HMS,

which is a hydrologic modeling software developed by the US Army Corps of Engineers Hydrologic

Engineering Center (HEC) is an integrated modeling

tool for all hydrologic processes of dendritic watershed

systems. It consists of different component processes

for rainfall loss, direct runoff, and routing. HEC-HMS

has become very popular and been adopted in many

hydrological studies because of its ability in the

simulation of runoff both in short and longtime events,

its simplicity to operate, and use of common methods

[12]. Hydrographs developed by HEC-HMS either

directly or in conjunction with other software’s are used for studies of urban drainage, water availability,

future urbanization impact, flow forecasting, flood

damage reduction, floodplain regulation, and systems operation [13].

The objective of this study is to apply GIS software

and remote sensing to determine Curve Number for

Beht watershed to study a rainfall-runoff model based

on the HEC-HMS, to calculate runoff volume and peak discharge.

2 Materials and methods

2.1 Study area

The Beht catchment area is located in northwestern

Morocco and covers an area of approximately 4560

km2 in the southwestern part of the Sebou basin. It is

bounded to the north by the Gharb plains and the

Meknes shelf, to the south by the Oum-Erbia basin, to

the west by the Bouregregreg basin and to the east by

the Middle Atlas. Its boundaries are located between

the meridians 5° and 6° West and the parallels 33° and 34° North.

This basin is located between the Lambert coordinates

(X1 = 430347.24; Y1 = 281864.43) and (X2 = 529704.23; Y2 = 386110.82).

Fig. 1. Geographical location of the catchment area of Beht

The Beht watershed has an elongated shape

following a SW-NE direction. The Gravelius index of

compactness, calculated for this basin, is about 1.86. It

is therefore eight times longer than it is large, which allows a rapid collection of water towards the outlet. It

can also be assimilated to a rectangle of the same

surface area, which is 202.5 km long and 22.5 km in

width. The Beht catchment area has a Mediterranean

climate (semi-arid to humid). It presents a double

gradient of decreasing intensity from South to North

and from East to West. This climate is marked by

frequent summer droughts and violent stormy rainfall.

Rainfall is marked by annual fluctuations. They vary

from 550 mm in the North-West of the basin to about

900 mm in the South-East. Temperatures show a clear

variation in space and time. High altitudes are characterized by low temperatures, ranging from -

0.9°C in winter to 25°C in summer. While low altitude

regions record temperatures of around 15°C in the winter and 34°C in the summer.

2


The hydrological regime is characterized on the one

hand by floods recorded mainly during wet periods and

which ensures 80% of the annual liquid flow. On the

other hand, low water levels during the dry season, when liquid flows are very weak at around 20%.

2.2 Data processing

The work methodology focuses on the preparation of

the data necessary for the spatial hydrological modeling of the basin, from Arc Hydro and HEC-

GeoHMS; extensions of a geographic information

system (GIS), as well as the elaboration of land use and

soil maps and the calculation of the Curve Number

grid, then the import of the basin model into HEC-HMS.

2.2.1 Delimitation of the watershed

The traditional method used to delineate a watershed

area from the topographic map takes time and is

inaccurate and it has been replaced by the automatic extraction from a digital Terrain Model [14].

So, the first step consists in the automatic

delimitation of the Beht watershed based on the digital

terrain model, derived from the ASTER sensor, which

is characterized by its 30 m spatial resolution. Then,

the delimitation of sub-basins and the extraction of the

drainage network from the basin DTM [15]. Nine

operations are carried out to get the schematization of the basin model [16].

2.2.2 Land use map

It is determined through a supervised classification on

satellite images "ASTER" using an image processing

tool (ENVI: Environment for Visualizing Images).

There are six main types of land use in the Beht

watershed: Pastures covering almost 1/3 of the area,

which is equivalent to an area of 1471 km2. They are

geographically dispersed throughout the basin. This

natural vegetation develops according to the type of

soil conditions and climate. Followed by bare land,

which representing 23% of the total surface area, i.e.

1052 km2. They are mainly located upstream. Forests represent 20.6% of the surface area. They are grouped

in two lots located respectively on the middle and the

southern extremity of the basin. The agricultural lands

represent 18% of the land; they are mainly located up-

stream of the basin. Matorrals appear in the extreme

northwest of the study area representing 5.6% of the surface area. (Fig.2)

Fig. 2. Land use map

Because of the specific requirements of the chosen

modular combination, specifically the NRCS CN

(Natural Resources Conservation Service Curve

Number) method as a production function, the

elaboration of a land use map over the entire study area was a necessary step.

The information contained in this map should be

authentic to the classification of the NRCS (Natural Resources Conservation Service)[17], so we had to

make connections between the NRCS classes and the

map prepared by the satellite image classification

method. Then, we reclassified the thematic classes defined above as shown in the table below:

Table 1. Land Use Class Reclassification

2.2.3 Soil map

The nature of the soil affects the rate of flood rise and

volume, as well as, the infiltration rate, moisture

content, storage capacity, initial losses, runoff

coefficient (Cr) are all related to the soil type.

The soil map was recovered from the National Institute

of Agronomic Research (INRA) (published in 2001)

(Fig.3), and digitized in order to get a standard soil

map. The main classes of soils outcropping in the Beht catchment area: Calcimagnesic soils (CAL.S),

First classification Reclassification

Class

Number

Class Name Class

Number

Class Name

1 Water 1 Water

2 Forest 2 Forested area

3 Reforestation

4 Bare Soil 3 Non-forested

area 5 Built

6 Low vegetation 4 Low vegetation

3


Isohumic soils (ISO.S), Crude mineral soils (CM.S),

Poorly developed soils (PD.S), Vertisols and

assimilated soils (VA.S), Fersiallitic soils (FER.S),

Hydromorphic soils (HYD.S), Brown soils (BR.S).

Fig. 3. Soil map of the study area (INRA, 2001: digitized)

The soil cover for the entire watershed shows a significant dominance of poorly developed soils, which

can form associations with crude mineral and

calcimagnesic soils (33.1%). The poorly developed

soils and the association "poorly developed soils and

raw mineral soils" are located in the upstream part of

the watershed; they are mainly associated with alluvial

deposits. The association "poorly developed soils and

calcimagnesic soils" is present mainly on the right bank downstream of the basin.

Approximately 30% of the watershed is covered by

brown soils and associations of brown soils and hydro-

morphic or poorly developed soils. These soils are

mainly present in the middle of the watershed,

although they can also be found further south in the

upstream part of the watershed.

In the northeast and southwestern extremities, vertisols and assimilated soils are present, with a

percentage around 11%. Isohumic soils and the soil

association isohumic and calcimagnesic soils

downstream of the watershed, have a proportion of

11.2%.

The basin has other types of soil and soil

association, such as: hydromorphic soils, fersiallitic

soils, the association of raw mineral soils, poorly evolved soils and hydromorphic soils, the association

of fersiallitic and poorly evolved soils, the association

of brown soils and raw mineral soils etc..., but in small

or even very small proportion.

The soil classification used by the Soil

Conservation Service method is the hydrological

classification. It is a classification that consists of

grouping soils into four hydrological groups (A, B, C,

D), [18], based on their estimated infiltration potential.

As a result, soils are assigned to one of four groups

based on their infiltration rate; A; soil having high

infiltration rates, B; soils having moderate infiltration

rates, C; soils having slow infiltration rates, and D;

soils having very slow infiltration rates [17].

The transition from soil classification to hydrological classification is made by providing

information on soil texture according to the

composition of sand(S), silt (St), clay(C) and organic

matter (O), because Soil texture information is

essential to determine the runoff coefficient [19]. The

values of these components are given in the following

table:

Table 2. Soils textural classes and its associations according to their correspondence in hydrological class

Soil name Area(%) Hydrological grp Texture

PD&CM 15.6 C StSC

BR 15 A StSC

PD 13.1 B StSC

BR&HYD 12.7 D C

VSA 10.8 D CStO

CAL&ISO 8 C StSC

CM 4.6 A StSC

CALPD 4.4 A StSC

ISO 3.2 C StSC

CAL 2.8 B StSC

BR&PD 2.7 C StSC

HYD 2 C StSC

FER 1.3 B StCS

CM&PD&HYD 1 B StSC

FER&PD 1 A StSC

BR&CM 0.6 B StSC

BR&CAL 0.4 D StSC

BR&ISO 0.3 C StSC

VSA&HYD 0.2 B C

VSA&PD 0.1 B C

4


Fig. 4. Pedology of the Beht Watershed according to hydrological classification

From this map it can be concluded that the most

dominant class is class C, which shows that the soils

have slow infiltration rates, therefore a relatively high

runoff.

3 Generation of the curve number map

3.1 Calculation of the CN grid

The SCS has developed a soil characterization system

based on the hydrology and land use group called the

Curve Number (CN). Values range from 0 to 100; A

CN value of 0 indicates no runoff potential, while a

value of 100 indicates that all precipitation runs off

[18]. In other words, a value of 100 is assigned directly to the water surface and 0 for highly permeable soils

with a high infiltration potential.

3.1.1 Preparation of the CN-Lookup table

The look-up table will contain the Curve Number for

different combinations of land use and soil groups. The

purpose of this table is to define the CN values for each

land use/hydrology group combination. In this case we

will use the SCS curve numbers that are available from

the literature (SCS reports, or SCS tables). The table

below summarizes the CN-Lookup table created from

the land use classes and their correspondence in

hydrological groups while following the TR-55

standard and the NRCS land use table:

Table 3. Attribute table of correspondence between land use

and soil type

Class

Number

Class Name A B C D

1 Water 100 100 100 100

2 Wooded land 45 66 77 83

3 Unforested land 77 86 91 94

4 Low vegetation 60 71 78 81

3.1.2 Creating the CN grid

HEC-GeoHMS is used to create the CN grid. It

combines the union result between type and land use,

the CN-Lookup table and the DTM of the basin. But

before proceeding, it is necessary to add a new field named "LandUse" in the union table. This field will

contain the land use category information, and will link

the union table to the CN-Lookup table.

Fig. 5. CN map of Beht watershed

The final map of curve number of Beht watershed

(Fig.5) shows an average CN of 78 which means that

the basin have a moderate high runoff, and this is due

to the clay type soil dominated by poorly developed soils, brown soils and vertisols assimilated soils and

also, the vegetation cover that is marked by significant

presence of pasture lands. These results are nearly

5


similar to the study realized by [20] who found that the

average curve number in the Sebou basin is 82.

The choice of the Curve Number depends, in addition

to the soil type and land use, on the antecedent soil

moisture conditions (AMC). These can be dry (I),

moderate (II) or wet (III) [21]. The values provided in

the Attribute Table (tab.3) are representative of average initial moisture conditions (CNII) (Fig.5) and the

Curve Number CNI and CNIII are calculated directly

using the [22] equations below:

𝐶𝑁(𝐼) =4.2×𝐶𝑁(𝐼𝐼)

10+0.058×𝐶𝑁(𝐼𝐼) (1)

𝐶𝑁(𝐼) =4.2×𝐶𝑁(𝐼𝐼)

10+0.058×𝐶𝑁(𝐼𝐼) (2)

Soil moisture status is determined based on

precipitation in the watershed during the last five days

before the event in question, and by season (low and rainy seasons).

The curve number in the conditions I and III are 51

and 89 respectively.

4 HMS model

The HEC-HMS model is physically based and

conceptually semi-distributed model designed to

simulate rainfall-runoff processes in a wide range of geographic areas, from large river basin to small urban

and natural watershed runoffs. It makes it easy to

perform huge tasks related to hydrological studies,

including losses, runoff transform, open channel

routing, weather data analysis, rainfall-runoff

simulation and parameter estimation [23, 24].

Moreover, the HEC-HMS uses separate models that

compute runoff volume, models of direct runoff, and models of baseflow. It has nine different loss methods;

some of it is designed for event simulations, whereas

others are for continuous simulation. It also has seven

different transformation methods, Clark Unit

Hydrograph Banitt [25] methods that have been

applied successfully to simulate long-term stream

flows.

In this study, the Soil Conservation Service Curve Number loss method was selected to estimate direct

runoff from a specific or design rainfall [26]. This

method normally calculate the runoff volume by

computing the volume of water that is intercepted

infiltrated, stored, evaporated, or transpired and

subtracting it from the precipitation. It was chosen

because it is a simple method for the estimation of the

direct runoff from a storm rainfall event, and it relies only on the curve number, which is a function of the

soil type and land use/cover that are the major runoff-

producing watershed characteristics and also it is

commonly used in different environments and provides

better results compared to initial and constant loss rate

method [27]. The SCS-CN method is based on the fact

that the accumulated rainfall-excess depends on the

cumulative precipitation, soil type, land use and the

previous moisture conditions as estimated in the

following relationship [21]:

𝑃𝑒 =(P−Ia)2

P−Ia+S (3)

Where Pe is the accumulated precipitation excess at time t (mm); P is the accumulated rainfall depth at time

t (mm); Ia is the initial abstraction (mm) = 0.2S; and S

is the potential maximum retention (mm).

The maximum retention, S, and watershed

characteristics are related through an intermediate

dimensionless parameter, the curve number (CN) as:

𝑆 = 25400 −254×𝐶𝑁

𝐶𝑁 (4)

Where CN is the SCS curve number used to represent the combined effects of the primary characteristics of

the catchment area.

Regarding the transform method, the Soil

Conservation Service Unit Hydrograph model was

chosen to transform excess precipitation into runoff.

The lag time (Tlag) is the only input for this method. It

is the time from the center of mass of excess rainfall to

the hydrograph peak and is calculated based on the time of concentration Tc, as:

𝑇𝑙𝑎𝑔 = 0.6𝑇𝑐 (5)

Where Tlag and Tc are in minute.

The routing method selected is the Muskingum

method, which was developed by McCarthy [28]. It

allows calculating the outflow hydrograph at the

downstream end of the channel reach from the inflow

hydrograph at the upstream end. Two parameters are needed; travel time (K) of the flood wave through

routing reach; and dimensionless weight (X) which

corresponds to the attenuation of the flood wave as it

moves through the reach. The routing parameters in the

models are usually derived through calibration using

measured discharge hydrographs [29].

4.1 Model Calibration and Validation

In order to identify the key parameters and parameter

precision required for calibration, a sensitivity analysis

is usually used in most modelling studies [30, 31]. The sensitivity parameters were selected based on their

effect on peak discharge and total volume. The model

calibration was done with the Univariate Gradient

optimization package and Peak-Weighted Root Mean

Square Error (PWRMS) objective function because of

their simplicity and performance [26]. In this study, six

flood events (four for calibration and two for

validation) in the period from 1996–2014 were selected for calibration and validation. Watershed parameters

such as curve number, initial abstraction, Tlag and

baseflow possibly will need modification to produce

the best fit between simulations and observations. For

validation, the simulated data must be computed and

compared with the observed data.

Statistical assessment criteria as relative bias error

functions proposed by Najim [32], Nash–Sutcliffe Efficiency (NSE) by Nash and Sutcliffe [33], Ratio of

standard deviation of observations to root mean square

error (RSR) by Moriasi et al. [34] and coefficient of

6


determination (R2) as described in Neter et al. [35]

were applied to evaluate the performance of the model

and the selected loss and transform methods.

5 Results and discussion

5.1 Calibration

The results of the hydrological model in this study

showed a reasonable fit between the simulated and

observed hydrographs after optimization; the shape of

the hydrograph and the time of peak had a good match

(fig.6). In the majority of events, the hydrograph shape

was accurately reproduced in the model output.

However, the volume of runoff was overestimated in

events 1, 2 and 3 and underestimated for the event 4. The modelling results of peak discharge, total volume,

and their relative errors with respect to the observed

data, the Nash Sutcliffe Efficiency, the coefficient of

determination and ratio of standard deviation of the

observation to the root mean square error (RSR) values

during calibration are mentioned in Table below.

Figure 7 (Event 1) show correlation values between the

observed and simulated flows (sample event).

Fig. 6. Simulated and observed hydrograph during

calibration for event 1.

Fig. 7. Correlation between observed and simulated

flow during calibration for event 1.

Table 4. Simulated and observed peak discharges and total volume and their evaluation criteria during calibration.

The calculated values of the percent error in total

volume and peak flow between simulated and observed

values in all simulations before optimization was approximately high, with mean values of 6.14% for the

peak flow and 14.15% for the total volume. According

to this result, a sensitivity analysis was conducted to

determine the most sensitive parameter. It was found

that the initial abstraction and curve number were more

sensitive, travel time K less sensitive and lag time was

insensitive.

After optimization, the values in this study were reduced to 0.1 to 6.1% for the peak flow and 0.86 to

17% for the total volume, with mean values of 2.5%

and 4.8%, respectively (Table 4). The result is very

good according to Najim et al. [32] and Cheng et al.

[36] who recommended that the acceptable ranges of

relative percent errors between the observed and

simulated values should be below ±20%. The results

showed also a relatively close agreement between the observed and simulated peak flow values at the period

of calibration (R2 = 0.842) (Fig.7). Based on the

classification mentioned in Zou et al. [37] the mean

correlation coefficient obtained in this study can be

considered as strong (>0.8). Regarding the Nash–

Sutcliffe Efficiency (NSE) criteria, better results were

obtained between the simulated and observed values,

with a mean NSE value of 81.5% (Table 4), which means that the model performs very well. The model

simulation can be judged as satisfactory if Nash–

Sutcliffe Efficiency is greater than 50%, good if it is

greater than 65%, and very good if it is greater than

75% [34]. The mean value of Ratio of standard

deviation of observations to root mean square error

(RSR) obtained was 0.1, so according to Moriasi et al.,

[34], the model can be said as satisfactory if RSR<=0.7. The four statistical evaluation criteria with

mean values of REP = 2.5%, REV = 4.8%, NSE = 0.815,

R2 =0.842, RSR=0.1 showed good simulation between

the estimated and observed values.

5.2 Validation

The table below shows the validation of the simulated

results (Table 5) of peak discharge, total volume, and

their relative errors, the Nash–Sutcliffe Efficiency

(NSE), the coefficient of correlation (R2) and the

(RSR) values. Figure 8 and 9 showed the simulated and observed hydrographs and their correlation

respectively (Sample validation events are presented).

0

50

100

150

200

250

300

350

400

Outflow Obs flow

R² = 0.9189

0

50

100

150

200

250

300

350

400

0 50 100 150 200 250 300 350 400

Ou

tlfl

owm

3/s)

Obs flow (m3/s)

7


Fig. 8. Simulated and observed hydrograph during

validation for event 5.

Fig. 9. Correlation between observed and simulated

flow during validation for event 5.

Table 5. Simulated and observed peak discharges and total

volume and their evaluation criteria during validation.

The result obtained shows that the simulated values are

a little close to the observed ones for all the events

(Table 5) with an overestimation for the simulated values in peak flow. The mean relative percent error

between the observed and simulated values of total

volume and peak flow is 9.6% and 1.69%, respectively.

In relation to the coefficient of determination there is a

quite close match between the observed and simulated

peak flow values during validation (R2 = 0.807).

Considering the Nash–Sutcliffe Efficiency (NSE)

criteria, the results obtained between the simulated and observed values is 63.4% and the RSR criteria is 36%

According to Moriasi et al. [34], the model simulation

can be judged as satisfactory. Usually, the statistical

evaluation criteria with mean values of REP = 9.6%,

REV = 1.69%, NSE = 0.63, and R2 =0.870, RSR=0.36

show good simulation between the estimated and

observed values.

The SCS loss and transform methods showed good results on the simulation of the validation events and

the results of the statistical evaluation criteria showed a

satisfactory performance of the HEC-HMS model in

predicting the peak flow and total volume in the Beht

watershed. To increase the performance of the model

in simulating the runoff, It is recommended to set up

more rain gauge stations in the basin because the use of

3 stations is not sufficient to reduce the effect of spatio-

temporal heterogeneity in precipitation, also for the

flow data which are not enough to estimate perfectly the flows at the outlet, it is necessary to set up more

hydrometric stations in the upper areas of the

catchment, and to identify regional valid unit

hydrographs and curve number as Zelelew[38] and

Hawkins [39] recommend.

6 Conclusions

Our project is a perspective study which is inserted on

the preparation of the data necessary as a first step for

the hydrologic simulation using HEC-HMS model.

The delimitation of sub-basins, the extraction of the

hydrographic network and the development of the soil

and land use databases was a very important step in

this study. These were used with ArcGIS and HEC-

GeoHMS to estimate the curve number in three states (CNI (dry), CNII (medium) and CNIII (wet)), and to

determine a curve number map that has been used

successfully for estimating surface runoff from the

Beht watershed. The results demonstrate that the

watershed is characterized by clay soil type dominated

by poorly developed soils, brown soils and vertisols

assimilated soils and a vegetation cover that is marked

by significant presence of pasture lands which occupy more than 32%, followed by forests, agricultural land

and bare land. The CN of the Beht watershed is

medium to high, with an average value of 78, which

means that the basin have a moderate runoff potential,

the most runoff-producing areas have a high runoff

coefficient.

The model calibration was used to optimize the

parameters and the most sensitive parameter in the simulation was the initial abstraction followed by the

curve number. After optimization the peak flow and

total volume of all events are very close to the

observations. The performance of the HEC-HMS

model was very good in terms of relative error

functions, Nash-Sutcliffe Efficiency, coefficient of

determination and Ratio of standard deviation of

observations to root mean square error RSR based on the selected loss, transform and flow routing methods.

Finally, the results obtained showed that the model

can be judged as valid and good in terms of evaluation

criteria, but we suggest to set-up other meteorological

and hydrometric stations in order to generate more

details and to increase the performance of the model in

the simulations.

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0

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outflow obs flow

R² = 0,870

0

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300

0 50 100 150 200 250

Sim

ula

ted

flo

w (

m3/s

)

Observed flow (m3/s)

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